compute_loglikeobs: Compute the log-likelihood of the observed data given PCA...

In HGray384/pcaNet: Probabilistic principal components analysis - covariance estimation and network reconstruction

Description

The log-likelihood of the data for probabilistic PCA is known to be multivariate Gaussian. Using this, one can check the log-likelihood value of the observed data values given the parameter estimates from the PCA model. This can be useful to compare different models.

Usage

compute_loglikeobs(dat, covmat, meanvec, verbose = TRUE)

Arguments

dat	matrix – the data matrix with variables in rows and observations in columns.
covmat	matrix – the estimated covariance matrix.
meanvec	numeric – the estimated mean vector.
verbose	logical – whether extra output should be displayed.

Value

the log-likelihood value

Examples

p <- 20
n <- 7
set.seed(10045)
X <- matrix(rnorm(p*n), p, n)
miss.inds <- sample(1:(p*n), (p*n)/4)
X[miss.inds] <- NA
M <- !is.na(X)
Nobs_i <- rowSums(M)
Mu <- rowSums(X, na.rm = TRUE) / Nobs_i
Mu2 <- rep(0, p)
covmat <- diag(p)
# using sample mean
compute_loglikeobs(dat=X, covmat=covmat, meanvec=Mu, verbose=TRUE)
# using zero mean
compute_loglikeobs(dat=X, covmat=covmat, meanvec=Mu2, verbose=TRUE)

HGray384/pcaNet documentation built on Nov. 14, 2020, 11:11 a.m.